relative and discounted cash flow valuation on swedish
TRANSCRIPT
Bachelor Thesis in Finance
Relative and Discounted Cash Flow
Valuation on Swedish Listed Companies
- How applicable are the methods to companies in different industries?
Authors: Simon Otterberg and August Zetterberg
Supervisor: Magnus Willeson
Examiner: Håkan Locking
Semester: VT19
Subject: Finance
Level: Bachelor
Course code: 2FE32E
Abstract
The purpose of this thesis is to look at how the two widely used valuation approaches Free
Cash Flow to Firm and Relative valuation can contribute to the explanation of market prices
of shares. The study also aims to investigate if it is possible to find any significant differences
between industries, while using the two valuation methods.
There are a large number of models that are used to value assets and corporations, which
have been used for a long time in the banking sector and similar contexts. It is widely known
that a single valuation method or model which could predict a future stock price is hard to
find or might even not exist. The study uses a quantitative method, in which we evaluated 36
Swedish companies, to be able to draw conclusions about the two valuation approaches.
Our results suggest that the calculated prices obtained from the two methods correlate with
the market price of the share, and that the result differ between different industries.
Keywords: corporate finance, finance, valuation, discounted cash flow, multiples, relative
valuation, free cash flow to firm.
Acknowledgments
We would especially like to thank Håkan Locking and Magnus Willeson for good guidance,
valuable comments and support. Without your help, this work would not have been possible.
Furthermore, we would like to thank our opponents and other people who have come with
constructive criticism and rewarding views during the work. During this time we have
learned a lot about the subject. We have also had a lot of fun during the time we wrote this
thesis. Thanks again.
Simon Otterberg & August Zetterberg, May 2019
Table of Content 1 Introduction......................................................................................................................................... 1
1.1 Background ........................................................................................................................................ 1
1.2 Purpose and Research Questions ...................................................................................................... 2
1.3 Problem discussion ............................................................................................................................ 3
1.4 Delimitations...................................................................................................................................... 4
1.5 Previous research .............................................................................................................................. 5
2 Theoretical framework ....................................................................................................................... 6
2.1 Fundamental Analysis ....................................................................................................................... 6
2.2 Financing costs .................................................................................................................................. 6
2.2.1 Cost of equity .................................................................................................................................. 7
2.2.2 Cost of debt ..................................................................................................................................... 7
2.2.3 Cost of capital ................................................................................................................................. 8
2.3 Growth rate ........................................................................................................................................ 9
2.4 Discounted Cash Flow valuation..................................................................................................... 10
2.4.1 Free Cash Flow to Firm ............................................................................................................... 11
2.5 Relative Valuation ........................................................................................................................... 12
2.5.1 Multiples ....................................................................................................................................... 12
2.5.1.1 Price / Earnings ......................................................................................................................... 13
2.5.1.2 EV / EBITDA.............................................................................................................................. 13
2.5.1.3 Price / Sales ............................................................................................................................... 13
2.5.1.4 Price / Book-value ..................................................................................................................... 14
3 Research Methodology ..................................................................................................................... 15
3.1 Research Approach .......................................................................................................................... 15
3.2 Research Strategy ............................................................................................................................ 16
3.3 Data ................................................................................................................................................. 16
3.4 Sample.............................................................................................................................................. 16
3.5 Method issues/bias ........................................................................................................................... 17
3.6 The valuation process ...................................................................................................................... 18
3.6.1 Free cash flow to firm ................................................................................................................... 18
3.6.1.1 Cost of Debt ............................................................................................................................... 19
3.6.1.2 Cost of Equity ............................................................................................................................ 20
3.6.1.3 WACC ........................................................................................................................................ 20
3.7 Regression specification .................................................................................................................. 21
3.7.1 Assumptions of Linear Regression................................................................................................ 21
3.7.2 Least squares estimation............................................................................................................... 22
3.7.3 Dummy variables .......................................................................................................................... 22
3.7.4 Hypothesis..................................................................................................................................... 22
3.7.5 Coefficient of determination, 𝑅2................................................................................................... 22
3.7.6 Confidence interval and P-value .................................................................................................. 23
3.8 Industry comparison ........................................................................................................................ 23
3.9 Reliability ......................................................................................................................................... 24
3.10 Source criticism ............................................................................................................................. 24
4 Empirical Method ............................................................................................................................. 25
4.1 Adjustment of data ........................................................................................................................... 25
4.2 Cost of equity ................................................................................................................................... 25
4.3 Weighted Average Cost of Capital .................................................................................................. 26
4.4 Growth rates .................................................................................................................................... 27
4.5 Valuation using FCFF ..................................................................................................................... 27
4.6 Valuation using Relative valuation .................................................................................................. 29
5 Results ................................................................................................................................................ 31
5.1 Free cash flow to firm ...................................................................................................................... 31
5.1.1 Industry comparison ..................................................................................................................... 33
5.2 Relative Valuation ........................................................................................................................... 35
5.2.1 Industry comparison ..................................................................................................................... 36
6 Discussion .......................................................................................................................................... 39
6.1 Method discussion ........................................................................................................................... 39
6.2 Result discussion .............................................................................................................................. 41
7 Conclusion ......................................................................................................................................... 43
7.1 Suggestion for Further Research ..................................................................................................... 45
References ............................................................................................................................................. 46
Appendix ............................................................................................................................................... 49
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1 Introduction
In this chapter, the authors present a background of the research topic. This is followed by a
presentation of the research purpose. A problem discussion is being included as well.
___________________________________________________________________________
1.1 Background
“A cynic is a man who knows the price of everything, but the value of nothing”.
-Oscar Wilde
All assets have some type of value. Being able to understand the difference between a share's
price and estimated value can lead to a certain kind of knowledge of how the price may
develop in the short and long run. Hägglund (2001) means that a proper analysis could give
advantages to investors, since it results in better knowledge about companies and stocks,
which makes it possible to locate stocks with higher returns. There are a large number of
models that are used to value assets and corporations, which have been used for a long time
in the banking sector and similar contexts. In the article Brief Considerations on Business
Valuation Methods written by Hermoza and Molina, the authors discuss various models
within corporate valuation. They mean that many new models have been developed in recent
years. These models do not always produce unique results. The authors argue that for this
reason, it is highly reasonable to review the most popular and currently used models.
Various investment philosophies and valuation methods have been discussed and tested as
long as there have been financial markets. Sweden got its first limited company in 1848, and
since then, the industry has developed more sophisticated methods for valuing assets and
corporations. In an article from Financial Times, we are introduced to various concepts of
picking stocks. Some investors use momentum strategies, while others use technical analysis.
It is, however, most common to consider the valuation process of stocks. If you make a
proper valuation of a share, you either might find a stock that you predict to trade higher than
the current price. You might also want to confirm that the stock you just bought is correctly
priced, to prevent unexpected events and thereby reduce the risk of your picked stock
(Financial Times, 2014).
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In Corporate valuation, there are two traditional concepts of valuation approaches, and each
type has its subtypes. Intrinsic, or fundamental, valuation is one of them. The other one is
called relative valuation, and the differences between these models originate from the diverse
views of market efficiency. In intrinsic valuation, one assumption is that markets make
mistakes. This means that the price of stocks in a sector, or even in a whole market, is not
necessarily correct. The stock could be either over- or undervalued. Regarding the relative
approach, Damodaran (2012) means that even though markets make mistakes in pricing
certain individual stocks, markets are correct overall. It means that when you compare a
specific stock to other comparable stocks, the price of the comparable stocks is correct on
average.
In intrinsic valuation, you specifically look at cash flow models. Fernández (2008) says that
the methods that are currently the most used are the discounted cash flow models. They are
also the most accepted methods in both the academic and business community. Generally,
these methods try to determine the firm value through the estimation of the cash flows that
will be generated in the future, and then discounting them at an appropriate rate according to
the risk of such flows (Fernández, 2008). This approach of valuing a company is not
dependent on a variable from any other company, and is instead based on the fundamentals of
the company itself.
If we instead consider the relative valuation approach for valuing a company, it is found that
the determination of a company's value is based solely on comparable firms. A comparable
firm is a firm with similar metrics as the company being valued. For instance, if company A
is being evaluated and company B and C have similar cash flows, risk and growth potential
as company A, and both have a P/E ratio of 10, the logic of relative valuation says that
company A should also have a P/E ratio of 10 (CFI, 2019).
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1.2 Purpose and Research Questions
The purpose of this study is to find out how significant the Free Cash flow to Firm approach
and the Relative valuation approach are to explain the market value of stocks. We want to
find out how the results that these models are generating differ from the market value of a
share, and further understand how the valuation methods are better used on companies in
different sectors. To fulfil the purpose of the study, we ask the following questions:
I. Can any of the valuation methods contribute to an explanation of the market price?
II. Are the two valuation methods more applicable to companies in different sectors?
1.3 Problem discussion
It is widely known that a single valuation method or model which could predict a future stock
price is hard to find or might even not exist. One can say that the search for information and
valuation models that determine the prices of shares in capital markets can be explained as
the pursuit of the Holy Grail (Ramnath et al., 2008). Professor Sven-Erik Johansson refers to
Hult (1998), who says that company valuation is complex;
"Let me start with a statement about which I think we can get general consensus: valuing
companies is not easy. It is primarily because in order to know what a company is worth
today, you need to know what will happen in the future”
However, some valuation models are preferred among others in businesses and the academic
world, which indicates that they are of practical use and thereby must have some advantages.
It is not always preferable to apply all models to all types of companies, since the
characteristics usually differ. Thus, there are companies with a particular type of character in
which a model can be of greater relevance than other models. Therefore, it is relevant to
investigate which model that works better on specific companies in different industries. We
want to find out when a model works better than another, and under what conditions it works
better. It is also relevant investigate to what extent our empirical study will be consistent
according to what has already been written in theory.
A model might give a result that is far from the current price of the share. There are two
possible explanations for that. It could imply that the model itself is not suitable to use of the
valued company. It could also mean that the market has over- or undervalued a company, and
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that the result of the model is precise. This is a paradox which many financial analysts and
corporate managers are struggling with. The issue is whether to rely on the results of the
model to make an investment action or not. Hermoza and Molina (2017) mean
that even though multiple methods can be used to evaluate a firm, the calculated values
obtained from the models result in a wide range of differences. The differences are reflected
in the final value obtained through the valuation exercise, and also in the conceptual
development throughout the stages of the valuation process. They also say that it is not
possible to discount any of these methods as incorrect, considering that the use of them
depends, in the majority of the cases, on the purpose for which the practice is conducted
(Hermoza, Molina, 2017). In a report by Jensen Investment Management, written by Kurt
Havnaer, he argues why they think that DCF is a more sophisticated way of valuing
corporations rather than using multiples. On the contrary to relative valuation, DCF makes
estimates of the total fundamental drivers of the company value. Havnaer also says that
making reasonable forecasts based on these fundamentals is more important than evaluating
the company based on multiples, where the drivers of the company value are simplified.
For the reason that there are several different ways of valuing shares, and that these differ
considerably between each other regarding approaches and the whole concept, there is a
reason to study these more closely. Further, we also want to see if the models are better
applied to companies in a certain type of industry.
1.4 Delimitations
This study aims to evaluate 36 Swedish companies listed on the Stockholm Stock Exchange.
The valuation will be done for five years, based on FCFF and Relative valuation. The reason
for this is that we consider it a reasonable amount of data to fall within the framework of the
thesis. The companies are divided into three sectors. In agreement with what is written in
theory, it can be complicated to put a value on companies that operate in markets with
uncertain growth and high volatility in revenues, and consequently, uncertainty regarding the
future cash flows. Including smaller companies with higher risk and uncertainty, would
complicate the valuation process, and the work over all. For that reason, companies with the
mentioned characteristics will not be included in this study. We choose to include listed and
mature companies, which according to the theory, should be better to apply our models on.
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1.5 Previous research
As earlier mentioned, corporate and stock valuation are key areas in banking, finance and in
the stock-market overall. Because of this, valuation and the methods used to make valuations
have been dealt with in many works of literature, articles and essays, and what is discussed in
this study is not new in itself. In the article Is cash flow king of valuations?, published in
Financial Analyst Journal, the authors suggest that valuations derived from industry multiples
are closer to traded prices than those based on reported operating cash flows. In the study,
they compared the valuation performance multiples on a large sample of companies from ten
different countries. Two measures of cash flow - operating cash flow and dividends, was
compared with earnings multiples. The main finding is that valuations based on industry
multiples using earnings forecasts are very accurate, and more precise than other multiples.
In the study Valuation Using Multiples - Accuracy and Error Determinants, written by Ek
and Lillhage, the authors conclude that the multiple approach yields relatively good estimates
compared to DCF. They found that eight out of ten multiples have a valuation error less than
15% on a long-term basis, and that four multiples outperform the DCF method. We are not
familiar with previous studies where specifically FCFF and the relative valuation approaches
were applied to multiple Swedish listed companies in the same study.
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2 Theoretical framework
This chapter gives a presentation of the general theory about the concept of valuation. These
terms and theories will frequently be used in this study. Therefore, the theories is of great
importance for the reader.
___________________________________________________________________________
2.1 Fundamental Analysis
Fundamental analysis in combination with a chosen valuation model is a process that will
lead to a certain result. This result should be the basis for the company's value. Nilsson
(2002) says that the result of the valuation process based on fundamental analysis leads to the
final price for the company. A fundamental valuation of a public company aims to determine
whether a company's shares are undervalued or overvalued against the listed market price for
the share (Nilsson, 2002).
“An investment in knowledge pays the best interest.”
– Benjamin Franklin
Valuation is in focus regarding the fundamental analysis. According to Nilsson (2002),
fundamental analysis refers to the process of determining the fundamental value of a stock
using publicly available information. Some analysts use discounted cash flow models to
value companies, while others use relative valuation. The underlying character for
fundamental analysis is that the right value of a company can be linked to its growth target,
risk profile and cash flows. When there is a difference between the market value and the
calculated value, it indicates that the share is under- or over-valued (Damodaran, 2012).
2.2 Financing costs
For a company to be able to develop and grow, they will need to invest in new assets. These
investments will cost money, and the company is dependent on financing to cover the costs of
the investments. Companies can receive money from two different sources of finance; lenders
or investors. These two groups provide the company with money, hoping to make a positive
return on their investment. There are two costs for the company. A cost arises when lenders
want to get paid for the risk of lending money to the company. Also, investors who buy
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shares in a company will expect a return on their invested capital, corresponding to the risk
associated with investing in the company (Damodaran, 2012).
One common theory while discussing the cost of financing, is the Pecking Order theory. It
was developed by Myers, Myers and Majluf in 1984. They argue that, because of asymmetric
information, firms adopt a hierarchical order of financing preferences so that internal
financing is preferred over external financing. If external financing is needed for a company,
they first seek debt for funding. Equity is issued as a last resort. According to this theory, a
company will always cover its external financing needs with debt, as long as their debt
capacity allows for it (Jong, Verbeek, Verwijmeren, 2011).
2.2.1 Cost of equity
Capital Asset Pricing Model (CAPM) has served as a basis for comparing price and risk since
the 1970s. The model is based on the idea of systematic risk (often referred to as non-
diversifiable risk) and that investors must be compensated for it in the form of a risk
premium. A risk premium is a return greater than the risk-free interest rate. When investing,
investors want a higher risk premium when they take on more risky investments (CFI, 2019).
Although the model has suffered some criticism, CAPM is still one of the most used models
to calculate the cost of equity. It is used mainly for applying to larger companies, but also for
smaller ones. It is also used on companies in the expansion phase and more mature ones
(Grabowski, Pratt, 2014). The CAPM approach is useful to estimate the Cost of equity, but
some scientists mean that it was first developed only for liquid assets (Michailetz,
Artemenkov, & Artemenkov, 2007). According to this, non-listed companies should therefore
be subject to further research, and should not be applied by the CAPM model (Steiger, 2008).
2.2.2 Cost of debt
The second financing cost which the company should consider is the cost of debt, and it is the
return that a company provides to its debtholders and creditors (Damodaran, 2014). The cost
of debt could be seen as the current cost of borrowing funds, which in practice is the average
interest rate the firm has to pay the lending companies. Miller and Modigliani (1963) mean
that a company's value increases with borrowing. This is because of the cost of debt, and the
fact that the interest rate is deductible. As a result, higher debt can result in a larger tax debt,
which increases the company's value. They later mean that economic theory and market
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experience both suggest that the yield that is demanded by lenders will increase with the
debt-equity ratio of the borrowing firm.
Miller and Modigliani (1963) later say that even though there is a tax advantage for debt
financing, it does not necessarily mean that corporations should always seek to use the
maximum possible amount of debt in their capital structure. This is because other forms of
financing, notably retained earnings, may in some circumstances be cheaper.
According to Damodaran, the Cost of debt is affected by three factors.
• The riskless rate: As the riskless rate increases, the cost of debt for companies will
also increase.
● The default risk of the firm: As the risk of not paying back the loan increases, the cost
of borrowing money will also increase.
● Tax benefits associated with debt: The interest is tax deductible; therefore, the after-
tax cost is dependent on the tax rate. This advantage arises from that the pre-tax cost
of debt is larger than the after-tax cost of debt. The interest expenses will decrease as
the tax rate increases (Damodaran, 2012).
2.2.3 Cost of capital
If you add the cost of equity to the cost of debt, you obtain the cost of capital. Rao and
Stevens (2007) are saying that the Cost of capital is perhaps the most fundamental and widely
used concept in financial economics. Pratt and Grabowski (2014) mean that the Cost of
capital is forward-looking. What they mean is that it represents the investor’s expectations.
They were continuing by saying that the Cost of capital is market driven, since it represents
the expected rate of return that the market requires. The actual Cost of capital for a specific
firm depends on how much of the financing costs are derived from the two financing sources,
lenders or investors. This is best shown by describing the Weighted Average Cost of capital
(WACC). The WACC has an essential role in every DCF-valuation process. Since is it used
as a discount rate, the WACC is very crucial for the predicted future cash flows.
WACC reflects the cost of own equity and borrowed capital (Cegłowski, Podgórski, 2012).
The WACC of a company is dependent on a variety of factors. For example, the industry
where the company operates, and the steadiness of its cash flows influence the cost of capital.
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Companies that are characterized with stable cash flows in mature industries, with low
growth rates, will typically have low capital costs (Morningstar, 2007)
According to Modigliani and Miller (1958), no mix of own and borrowed capital is better
than another. Their theory suggests that it does not matter if a specific company is only
financed by equity, or if the capital consists of a mix of different loans and equity. The
company value will still be as large. This contradicted what economist previously believed in
this area. According to their theory, the person who want to invest in a company does not
have to consider whether the company is financed with own equity or debt capital.
2.3 Growth rate
Steiger (2008) means that small changes in the underlying assumptions of the valuation will
result in large differences in the company’s value. It is therefore of great importance to know
which assumptions are used, and how they influence the outcome valuation. In the free cash
flow to firm model, the growth rate used plays an important role, since the growth rate that is
assumed can have a dramatic impact on the terminal value, and therefore the firm value.
"The investor of today does not profit from yesterday’s growth."
- Warren Buffett
Valuations can be made both by using one single growth rate, or multiple growth rates. It
depends on whether the company is already mature in the market or if they are still in an
expansion phase. In discounted cash flow valuation, it is common to divide the growth into
two stages. One represents the high growth phase, where a company entering a new market
succeeds to attract customers. This will generally lead to relatively high revenues in which
the company in question is said to be in the high growth phase. At some point, the company
will pass over to a stable growth phase, and this period is assumed to last as long as the
company operates on the market. According to JP Morgan Chase, the stable growth rate
should be equal to the nominal growth in GDP.
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2.4 Discounted Cash Flow valuation
The discounted cash flow models are based on the assumption that the value of a company is
the present value of the expected future cash flows (Kumar, 2016). Damodaran argues that
the discounted cash flow valuation is the foundation on which all other valuation approaches
are built on and that when holding other things equal: higher cash flows, higher growth and
lower risk should result in a higher value of the company (Damodaran, 2012).
Ceglowski and Podgorski (2012) mean that from a Value Based Management perspective, as
well as the analysis related to fundamental analysis, the present value of cash flow that a
given enterprise can obtain due to operational and strategic decisions is the most important
value. Therefore, they mean that regarding company valuation, income-based methods are
preferable, especially techniques of discounted cash flow (Cegłowski, Podgórski, 2012).
The three basic models of DCF valuation are the Free Cash Flow to Equity (FCFE), the Free
Cash Flow to Firm (FCFF) and Dividend Discount Model (DDM). For FCFF, which this
study will focus on, the future cash flow is discounted to the main stakeholders (shareholders
and debtholders) with the Cost of capital (WACC) to arrive at the value of the company
(Kumar, 2016). Theoretically, the models' approach can vary a little, even if they are based
on discounting future cash flows. FCFE is based on evaluating the company's equity, while
FCFF is about valuing the entire company. FCFF therefore includes, in addition to equity,
bondholders and preferred stockholders. The DDM model is a special case of valuation,
where the value of equity is the present value of future dividends. The differences between
FCFF and FCFE is applies mostly regarding the cashflows associated with debt - interest
payments, new debt issues and principal repayments (Damodaran, 2012).
Fernandez (2008) mean that the most appropriate method to value a company is to discount
the expected future cash flows. Hermonza and Monina (2017) agree with Fernandez and say
that these methods present many advantages. The methods are considered dynamic because
the firm value depends on its capability to generate funds in the future, and it is not limited to
performing a static analysis considering only the historical information of the organization.
These methods are not based on subjective perceptions by their owners or market potential
buyers (Pereyra, 2008).
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Penman (2006) highlights some criticism of the DCF models. In his article, he refers to a
fictitious example to demonstrate that the models do not always work considering valuation.
Penman holds a company that has negative cash flows four of the last five years. Applying a
growth rate to negative cash flows leads to model failure, which is why he thinks free cash
flow is an unreliable indicator of value.
2.4.1 Free Cash Flow to Firm
In corporate practice, the FCFF technique is most commonly used. Mielcarz and Mlinaric
(2014) say that according to the financial analyses and the popularization of particular
techniques, in the literature, three techniques can be recognized as the most essential, where
one of them is the free cash flow for firm.
As mentioned, FCFF is about valuing the entire company. FCFF therefore includes, in
addition to equity, bondholders and preferred stockholders (Damodaran, 2012). It allows the
analysis to be performed from the point of view of all parties financing. Damodaran (2012)
means that a high FCFF indicates that the company has money left behind for its operations
and performance, and at this point suggests good economic health for the company.
According to Damodaran, the applicability of DCF models, where the FCFF approach is
included, depends on the informational requirements of expected future cashflows and
discount rates. This approach is easiest to use for firms with currently positive cashflows and
with some degree of reliability of estimating future cashflows. It also requires a proxy for risk
that can be used as a discount rate for the company. If these information requirements are not
fulfilled, the difficulties of making an objective valuation increase. The biggest problem
occurs when evaluating non-listed private companies since their securities are not publicly
traded and cannot be measured in terms of risk, and therefore affect the possibility to obtain a
fair discount rate. Contrary, this approach is best applied when evaluating large, listed
companies where a great amount of information is available.
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2.5 Relative Valuation
In corporate valuation, the relative valuation approach is used to evaluate companies by
comparing them to other businesses based on certain multiples (CFI, 2019). The most
common use of this approach is to use an industry-average ratio to obtain a firm value. This
assumes that the other firms in the industry are comparable to the firm being valued and that
the market, on average, prices these firms correctly (Damodaran, 2012). Relative valuation is
also known as comparable valuation.
There are two components to relative valuation. The process of relative valuation starts with
the selection of a peer group. Peer group selection is based on defining industry attributes,
matching companies on size, growth, margins, asset intensity, and risk. Multiples are
classified as earnings multiples, book value multiples, revenue multiples, and sector-specific
multiples (Rajesh, 2016). The second component is that to be able to value assets on a
relative basis, prices must be standardized. You can convert prices into multiples of earnings,
book values or sales. Unlike discounted cash flow valuation, which is described as a search
for intrinsic value, we are much more reliant on the market efficiency when using relative
valuation. In other words, we assume that the market is correct in the way it prices stocks on
average, but that there may be individual shares that are incorrectly priced (Damodaran,
2012). To detect these specific stocks, the key ratios in the relative valuation are used, and the
shares that are overvalued or undervalued could, therefore, be found. Damodaran says that
relative valuation could sometimes be difficult, since it is hard to find similar companies. No
two firms are identical, and firms in the same business can still differ on risk, growth
potential and cash flows. He says this part of the relative valuation is “a key one”, and that
ignoring these could be a potential pitfall for the valuation.
2.5.1 Multiples
The choice of multiples should be carefully selected before performing the valuation. In
performing a relative valuation, it is advantageous to use multiples that are based on different
fundamentals. In this case, the four multiples are based on earnings, book value and sales.
This is because the valuation is to be carried out as accurately as possible. The three most
widely used equity multiples are price-earnings ratios, price to book value ratios and price to
sales ratios (Damodaran, 2012). A valuation based exclusively on multiples that are for
example, earnings, should be considered inefficient. In an article written by Penman (1996),
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he writes about combining multiples, and how to weigh these against each other. One thing
that is mentioned is that a larger weight of the book value, in comparison to price weights,
indicates that the book value is given importance in predicting long-term results.
2.5.1.1 Price / Earnings
The price-earnings multiple (PE) is the most widely used of all multiples. The PE ratio is
consistent, with the value of equity per share in the numerator and earnings per share in the
denominator. Both of which are a measure of equity earnings (Damodaran, 2012). Looking at
the P/E ratio of a company tells you nothing if it is not compared to other company's P/E
ratios or the historical P/E ratios of the firm. The benefit of this multiple is that it standardizes
stocks of different prices and earnings levels.
2.5.1.2 EV / EBITDA
A firm value multiple that has won popularity in the last two decades, according to
Damodaran, is the Enterprise value to EBITDA multiple. This multiple relates the total
market value of the firm and the net of cash, to the earnings before interest, taxes and
depreciation of the firm. There are a few advantages of this multiple; there are fewer
companies with negative EBITDA than for example negative earnings per share, which
allows including more companies in the analysis. Secondly, companies have different
depreciation methods which will affect the earnings. By using earnings before depreciation is
considered, we erase these differences and the metric holds for all of the companies being
measured (CFI, 2019).
2.5.1.3 Price / Sales
The Price to Sales multiple compares a stock’s price to its revenues. This multiple, also
known as the market capitalization to revenue ratio, is of central interest in many areas of
capital market investment analysis and research (Armstrong, Davila, Foster, Hand, 2011).
The ratio shows how high the market values a company's revenue. That a company has a high
P/S number can mean two different things. Either that the company has a high share price in
relation to sales per share, which may indicate that it is highly valued. It could also mean that
the market has high expectations for the company, and believes that their sales will increase
in the future (Avanza, 2019).
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2.5.1.4 Price / Book-value
The Price to Book ratio is a multiple used to evaluate a company’s current market value
relative to its book value. It is used to compare a company's available net assets, in relation to
the sales price of its stock. Damodaran (2012) means that the book value provides a relatively
stable measure of value that can be compared to the market price, and says that price-book
value ratios can be compared across similar firms for finding indications of under or
overvaluation. Finally, firms that have negative earnings, which cannot be valued using P/E-
ratios, can be evaluated using price-book-ratios.
15
3 Research Methodology
This chapter contains choice of approach and strategy to work on this paper. Methods that
the thesis is based on, followed by the choice of data and source criticism.
___________________________________________________________________________
3.1 Research Approach
There are two different approaches when dealing with research questions; deductive and
inductive approach. The deductive approach is shortly summarized as testing an existing
theory. This approach generally suits most quantitative researches where numerical data is
analyzed. You primarily look at a specific theory, then formulating a hypothesis which can be
tested empirically by collecting data to prove or disprove the relevance of the theory
(Saunders, Lewis, Thornhill, 2009).
The deductive process can be described in four consecutive steps:
1. Deducing a hypothesis: Conduct a testable proposition of a relationship between
variables or concepts from the theory.
2. Expressing the hypotheses in operational terms: This mean that the variables or
concepts must be defined how they are to be measured.
3. Testing these operational hypotheses.
4. Analyzing the outcome of the test: are the results confirming the theory or rejecting it,
which then will imply a modification of the theory?
Inductive approach, on the other hand, may be seen as the opposite way of conducting
research; reversely, the first step is to collect data, then analyzing the results of the data which
ultimately will conduct the theory of the chosen research area (Saunders, Lewis, Thornhill,
2009).
The two valuation approaches we are investigating in this thesis are well described in theory.
The aim of the thesis is not to develop new theories about the subject, but rather to test and
provide support for the already existing ones, which is in line with the deductive approach.
Therefore, we will follow the deductive approach in our empirical section, since the aim of
this thesis is to test whether the two chosen valuation approaches are of good use in corporate
valuation.
16
3.2 Research Strategy
In order to answer the research question, an appropriate research strategy must be chosen.
The researcher can use a variety of approaches, which can be divided into two major
categories; qualitative or quantitative approach (Backman, 1998). Which method that should
be applied is very dependent on the type of examination to be done, and what the research
question is.
To test the research question in this thesis, a large amount of data needs to be collected, and
later on will be statistically analyzed. Quantitative research is more fitting than qualitative in
this case. Quantitative methods mean that the collection of information takes place in a
structured manner, and that they are characterized by control from the researcher's side.
Conducting surveys and directly contacting companies give different information than what
the numbers of their financial statements do. However, this thesis does not examine anything
depending on something else than the information from financial statements. A quantitative
approach which allows the authors to analyze a large sample of companies is, therefore, more
suitable than the qualitative approach. The information can then be converted into numerical
values, which are then analyzed - for example in diagram form or via statistical software
(Holme & Solvang, 1997).
3.3 Data
Bryman (2012) says that the process of data collection is of great importance for the
methodological framework. Our theoretical frame of reference is based on secondary sources.
Secondary data is collected, for example annual reports and financial key figures. The data
will be obtained from the database Thomson Reuters Eikon. In general, it is much less
expensive to use secondary data than to collect the data yourself. Consequently, you may be
able to analyze more extensive data sets (Saunders, Lewis, Thornhill, 2009)
3.4 Sample
In order to fulfil the purpose of this study, we will apply valuation models from financial
theory to 36 Swedish listed companies. These companies are taken from the Stockholm Stock
Exchange's Mid- and Large-cap list. We have divided these companies into three different
industries. These industries are Bank/Real Estate, Consumer-goods, and Manufacturing
17
companies. The companies will be divided into these industries, to be able to distinguish
differences between them.
Bank/Real Estate Consumer-goods Manufacturing
COLLECTOR LAMMHULTS DESIGN GROUP AUTOLIV
AVANZA BANK NEW WAVE GROUP SANDVIK
FABEGE AGROMINO HALDEX
SEB MQ HOLDING ASSA ABLOY B
PLATZER FASTIGHETER BILIA FAGERHULT
VICTORIA PARK CLOETTA NEDERMAN HOLDING
KUNGSLEDEN MEKONOMEN LINDAB INTERNATIONAL
CATELLA CLAS OHLSON ASTRAZENECA
CASTELLUM AXFOOD NIBE INDUSTRIER
HUFVUDSTADEN ELECTROLUX ATLAS COPCO A
WIHLBORGS FASTIGHETER ICA GRUPPEN ABB LTD
SVENSKA HANDELSBANKEN THULE GROUP ALFA LAVAL
3.5 Method issues/bias
Regarding the relative valuation, some theory suggests that a comparative company does not
need to be in the same industry at all. When performing a relative valuation, you should not
necessarily look at companies that are in the same industry, but rather look for similar
companies that have the same cash flow, growth rate and risk. Damodaran says, however,
that most analyzes use companies that are in the same industry to perform a relative
valuation. For this reason, this valuation will be executed based on industry. The companies
are compared to the industry average and are divided into different segments.
In most valuations, it is necessary to make assumptions. If we were only evaluating one
company, we could focus on achieving more precise underlying numbers. In the FCFF
valuation, we are forced to assume one high growth phase and one stable phase for all
companies. The reality is more complex than that, and the valuation could have been done
more precisely if we were including fewer companies.
18
3.6 The valuation process
The choice of valuation approaches is based on their differences. To be able to produce useful
results, and to be able to discuss the fundamental differences, we predict that two methods
based on completely different grounds can provide useful material. We will look at data to be
able to evaluate the companies five years ahead, starting with 2014. With this strategy, we
will be able to compare each company over multiple years.
It is not always common to put a specific value on a share while using relative valuation. You
compare with a benchmark of the industry and make decisions as to the extent whether the
company in question is under- correctly- or overvalued. We have chosen to interpret what
Penman suggests in his article and put greater weight on the P / BV-multiple. We will use this
as the basis for our valuation model. The distribution becomes P / BV = 40%, P / S = 20%,
P / E = 20% and EV / EBIDTA = 20%.
As earlier mentioned, the theory suggests that the best approach is to not rely exclusively on
one multiple. You should instead combine several different ones to get a weighted result that
is more accurate. All multiples have different advantages and combining them results in a
more trustworthy valuation. We have chosen to combine the multiples mentioned in the
theory section to be able to arrive at a company value for each of the companies involved in
the study.
3.6.1 Free cash flow to firm
Free cash flow to firm (FCFF) is the cash flows available in the company after taking into
account taxes, depreciation, changes in working capital and investments. It is essentially
possible to see FCFF as a measure of a company's profit after all costs and reinvestments
have been made. There are several formulas for calculating the FCFF at a specific time,
depending on which information is available for the estimate. In this study, are the following
formula will be used:
FCFF= EBIT(1 - Tax rate) + Depreciation - Capital expenditure – 𝛥𝑊𝑜𝑟𝑘𝑖𝑛𝑔 𝐶𝑎𝑝𝑖𝑡𝑎𝑙
EBIT refers to the earnings before interests and taxes and it is deducted by the corporation
tax. Depreciation is a non-cash expense, which is added back in the calculation and by
19
considering the investments and changes in working capital, we arrive at a final free cash
flow to firm value.
To calculate the value of the firm, the future expected cash flows are summarized, and
discounted with the Weighted Average Cost of Capital as following:
Damodaran suggest that two conditions that need to be met while using the stable growth
model, which is used for calculating the terminal value. First, the growth rate has to be less
than or equal to the growth rate in the economy. Second, the characteristics of the firm must
be consistent with assumptions of stable growth. What he means by that is that the
reinvestment rate used to estimate free cash flows to the firm needs to be consistent with the
stable growth rate. To calculate the terminal value using FCFF, we assume a stable growth
rate:
FCFF1 = The expected cash flow next year
gn = Growth rate (forever)
WACC = Weighted Average Cost of Capital
The value of the firm can now be calculated through FCFF:
3.6.1.1 Cost of Debt
The cost of debt refers to the effective rate a company pays on its current debt. It is obtained
by dividing the total interest expense with the total debt as followed:
Cost of debt = Interest expense / Debt
20
3.6.1.2 Cost of Equity
In order to determine the Cost of equity for our chosen companies we have got to consider
the Risk free rate(Rf), the Market Risk Premium(MRP) and the Beta values(β) for each
company. This is necessary to decide the CAPM parameters, which will be used to define the
Cost of equity.
The formula can be written according to the following Ri = Rf + (Rm-Rf) bi
Where
Ri = The expected return for shares in year i
Rf = A risk-free asset's expected return.
Rm = Expected return on the entire stock market.
bi = Beta value.
3.6.1.3 WACC
Weighted Average Cost of Capital is a calculation of a firm’s cost of capital where each
category of capital is proportionately weighted. A firm’s WACC increases as the beta and
rate of return on equity increases, since an increase in WACC mean a decrease in company
value and an increase in risk. Furthermore, a decrease in tax rate will increase the cost of debt
and thereby the cost of capital.
The formula for WACC is given by:
Where
𝐸 =Market value of equity
𝐷 =Market value of debt
𝑉 =E+D = Total market value of the firm
Re = Cost of equity
𝑅𝑑 = Cost of debt
𝑇𝑐 = Corporate tax rate
21
3.7 Regression specification
In this study, it is relevant to study the relationship between the calculated share value and the
actual market value of the share. As previously mentioned, we want to investigate to what
extent the calculated value is in correlation with the market value. Regression analysis is a
statistical method that attempts to explore and explain the relationship between two or several
variables. The general formula for the single regression model is:
Y = a + bX + 𝜀
where X is the independent variable, and Y is the dependent variable and 𝜀represent the
error/residual term of the regression.
In the regression in this thesis, the preliminary formula used is lnmprice = a + lnkprice + 𝜀.
Lnmprice represents the dependent variable market price. The independent, or explanatory
variable lnkprice, represents the calculated value of the stock. When performing our
regression analysis, we used logarithmic variables since the observations tend to deviate from
the regression line when having higher prices. This means that the variance of our regression
line increases as we move from lower to higher values, and the assumption of
homoscedasticity is violated.
3.7.1 Assumptions of Linear Regression
In linear regression, five assumptions is implied for relying the outcome of the regression
model. In this thesis, two of them are relevant to describe, since they could have potential
effects on the results.
-Linear relationship
The natural meaning of linearity is that the conditional expectation of Y is a linear function of
X (Gujarati, Porter, 2009). In linear relationships, any change in the independent variable will
produce a corresponding change in the dependent variable.
-Homoscedasticity
When the Y populations corresponding to X values have the same variance. If this
assumption fails, the model suffers from the opposite, heteroscedasticity, which leads to
biased standard errors and inefficient estimates.
22
3.7.2 Least squares estimation
One way of estimating 𝛽1, the unknown coefficient of our regression, is to use the Ordinary
Least Squares (OLS) estimation. The method minimizes the sum of the squared residuals in
order to optimize the estimate of the regression (Gujarati, Porter, 2009).
3.7.3 Dummy variables
The most common way of comparing between different industries in a regression model,
would be to use dummy variables. A dummy variable is a nominal scale variable that makes
it possible to compare attributes or characteristics between for instance industries by setting
values of 0 or 1. 0 indicates the absence of a certain attribute (or industry) while 1 indicates
the presence of the attribute (Gujarati, Porter, 2009).
3.7.4 Hypothesis
In order to make conclusions about the relationship between our predicted values and the
market values or measuring how significant the differences between industries in the
regression models, hypothesis testing is the common approach. We present the hypotheses of
our content, which will bring us closer to answering the research questions of this thesis.
3.7.5 Coefficient of determination, 𝑹𝟐
While estimating regression coefficients, we have to consider the goodness of fit of the
regression line. That means that we need to find out how well the sample regression line fits
the data (Gujarati, Porter, 2009). What we want is that the residuals around the regression line
are as small as possible. This determinant tells us about the proportion of which the variance
in the dependent variable is predictable from the independent variables. This measure can be
23
seen as an idea of how many data points would fall within the results of the line derived from
the regression equation. Overall, you could say that the higher the value, the better the model
fits the data.
3.7.6 Confidence interval and P-value
In hypothesis testing, the confidence interval is used to test the null hypothesis. When
discussing hypothesis testing, the confidence interval could also be known as the acceptance
region. Imagine a normal distribution curve, where the upper and lower limit is the critical
values. Kenton (2019) means that confidence interval is a way to with certainty know that a
variable is going to have its value in the acceptance region. In this thesis, a 95% confidence
interval will be used. This gives the used variable a 95% certainty to have a value within the
acceptance range.
The P-value, or probability value, could be defined as the lowest significance level at which
the null hypothesis can be rejected (Gujarati, Porter, 2009). If the chosen significance level α
is larger than the estimated p-value, then you could say that the null hypothesis should be
rejected. When the confidence level of 95% is used, the P-value should be below 0,05 to be
statistical significance.
3.8 Industry comparison
In this section, we will present differences and similarities between the three chosen
industries we run in three separate regression models. We want to see how the individual
industry affects the market price when only regressing the industry against the market price.
The models are conducted by putting a dummy variable for each industry with its associated
observations.
The progress can be summed up with the following formula:
log 𝑚𝑝𝑟𝑖𝑐𝑒 = 𝛽0 + 𝛽1 log 𝑘𝑝𝑟𝑖𝑐𝑒 𝑖𝑓 𝑖𝑛𝑑𝑢𝑠𝑡𝑟𝑦 * = 1
where industry* refers to the specific industry being tested.
24
3.9 Reliability
One of the things that measure quality in a quantitative study is reliability. This could be
explained as the accuracy of an instrument (Twycross, Heale, 2015). Ejvegård (2003) means
that in order for the survey method, the test, or the measure to be usable, it is required that it
is reliable and valid. If these two requirements are not met, the research result does not have
any scientific value. Bell (2000) argues that the measurement that is made must produce the
same result at different times, where there are similar conditions as in the first measurement.
By clearly explaining our approach and methodology regarding the valuation and the
regression analysis, we can increase reliability in the thesis. The weakness regarding the
credibility of this paper is that there is always a subjective aspect of the valuation of
companies. As earlier mentioned, it’s necessary to make certain subjective judgments to be
able to perform the valuation. If the authors have different views on different variables, the
result and conclusion can be affected by this. The subjective approach makes it hard to reach
completely unbiased calculated prices, and therefore an unbiased final result.
3.10 Source criticism
It may seem simple to understand the description of the theoretical models' substantiated
assumptions and functionality, but it may prove to be more complicated when these are to be
applied in practice. For this reason, we would like to have, to some extent, a skeptical
approach to the simplified models described in the literature. We want to investigate to what
extent the literature's approach is in line with the practical approach.
All of our values are based on secondary data obtained from the companies' annual reports.
The companies that are included in the study, should be considered large and mature in
comparison with other companies. It is necessary to believe that the companies comply with
generally accepted accounting principles, and is following the norms and rules that apply to
accounting standards. Therefore, it is necessary to assume that the financial numbers and
figures presented are correct and give a true and fair view of the company’s financial status.
We have also collected secondary data from doctoral dissertations, scientific literature and
journals. We consider the literature we use to be reliable. This is because the authors of the
literature are well known in the field, and have long been leaders in the subject. It is also a
fact that the content in the chosen literature could be seen in other literature as well.
25
4 Empirical Method
In the thesis empirical chapter, the results from the valuations, and the calculations
regarding these, could be found.
___________________________________________________________________________
4.1 Adjustment of data
In the regression, some companies may be excluded. This is because the company in question
has either negative cash flows, negative earnings or abnormal ratios. If the calculated value
for Company A differs by more than 300% from the market value of the share, Company A
will not participate in the regression. This is because we assume in such a case, that the
model is not applicable at all to that specific company. Furthermore, a minor data loss occurs
as a result of a few companies trading at negative P/E numbers. Since in most cases it is
unnecessary to relate companies to losses, these companies have been excluded. An example
of this is which has negative earnings, and therefore also a negative P/E-ratio. Due to the
broad spread of companies, the key figures results in a large spread, and extreme values are
created in some sectors.
4.2 Cost of equity
In most risk and return models, in which the CAPM is included, there is a need to define the
risk-free rate. One common way to determine the risk-free rate is through looking at the yield
of government securities. Accordingly, we obtained the risk-free rate by looking at how
Swedish government bonds have yielded over the past five years. The average rate of
government bonds is 0,98%, which was used in the CAPM model. The Market premium was
established by looking at the development of the market risk premium from 2014 to 2018,
which was presented in a report conducted by audit firm Pwc. The survey showed that the
market premium had not changed significantly over the years. To fulfill the market premium
of the CAPM calculations, we use the arithmetic average of the past market premiums
reported, which would be 6,36%.
Finally, to estimate the Beta values we obtained the past five-year Beta values for each
specific company, and made an arithmetic average of them to meet the criteria of making the
CAPM model. Thereby, it is possible to determine the Cost of equity for each company.
26
An example of how we calculated the CAP model is shown below (Beta for Axfood, 0,496)
𝐶𝑜𝑠𝑡 𝑜𝑓 𝑒𝑞𝑢𝑖𝑡𝑦 𝐴𝑥𝑓𝑜𝑜𝑑= 0,00977 + 0,0636 ∗ 0,496 = 0,0413
To determine the cost of equity for future valuation, we fix the cost of equity to the five-year
average cost of equity. For instance, the average cost of equity for Axfood is set to 0,0413.
4.3 Weighted Average Cost of Capital
As been previously explained, the weighted average cost of capital represents the total cost of
financing a company, where the cost of equity and cost of debt are associated with the
proportion of equity and debt. To determine the cost of debt for a company, we divide the
total interest expense of debt with the total amount of debt for five consecutive years from
2014 to 2018. The results are then averaged arithmetically to determine the cost of debt for
every specific company.
To determine the equity and debt for each company, we similarly averaged the previously
proportion of the two financing sources as a basis for the valuation. Subsequently, the tax rate
has to be determined in order to find out the tax advantage associated with debt. For the
process of evaluating our companies we have determined the tax rate to be 24,9%. This is
based on the fact that it is the average effective tax rate in the industries that are included in
this thesis (Thomson Reuters, 2019).
After all parameters for the WACC-formula are settled, we can determine the total Cost of
capital for each company. An example of how a calculation was conducted is shown below.
𝑊𝐴𝐶𝐶𝑆𝑎𝑛𝑑𝑣𝑖𝑘= 0,977 ∗ 0,0413166 + 0,022 ∗ 0,024 ∗ (1 − 0,249) = 4,08%
As shown, the cost of capital for Axfood is fixed to 4,08% when discounting future cash
flows in order to complete the firm valuation. Since Axfood has not made major changes in
its capital structure during 2011-2018, this rate will be used for all years.
27
4.4 Growth rates
To make a correct and precise valuation, it is important to set an appropriate growth rate for
each company. In this study, we have used historical growth rates estimated by arithmetic
averages to find a suitable high growth rate for the FCFF model. The high growth rate is
taken from the mean of the previous five-year revenue development. Even though there are
several measures of growth rates, we find that revenue is a good estimator of growth since it
is not affected by any accounting technique or other external effects. This will, however,
result in companies having negative growth rates when predicting future cash flows during
the high growth phase.
The high growth phase is assumed to last five years from the year of valuation. To determine
the stable growth rate for all the evaluated companies, we have looked at the GDP growth of
Sweden and set the mature growth to two percent. This is in line with the mean BNP
historical growth rate of Sweden since 1970 (Ekonomifakta, 2019), and also in line with what
J P Morgan suggests.
4.5 Valuation using FCFF
The final parameter that has to be decided in order to evaluate a firm according to the FCFF
model is the cash flow itself the firm has left after accounting for different outflows of cash.
As our theory section suggests, the formula we have used to determine the free cash flow to
the firm is:
FCFF= EBIT (1 - Tax rate) + Depreciation - Capital expenditure - 𝛥 𝑊𝑜𝑟𝑘𝑖𝑛𝑔 𝑐𝑎𝑝𝑖𝑡𝑎𝑙
To clarify how our calculations and implementation have been carried out, we explain the
2018 valuation process for Sandvik.
The figures below show values that are taken from Thomson Reuters database from 2015 to
2017. The tax rate (24,9%) is based on an average of the industry. FCFF is expected to grow
at a rate of 4% during the high growth phase, then decrease to a stable growth rate of 2%.
FCFF year 0 has been calculated by an arithmetic mean of Sandviks FCFF from 2015 to
2017, and has been determined to 9960 million SEK.
28
Year EBIT x (1-
0,259)
Depreciatio
n
Capital
expenditures
Change in Working
capital
FCFF
2015 5398188 535000 −4214000 -776000 943188
2016 8288036 4715000 −3701000 338000 9640036
2017 13814645 4936000 −3590000 4136000 19296645
Avg 99599563
FCFF PV
FCFF0 9959956 9320463
FCFF2019 10305567 9024685
FCFF2020 10663170 8738293
FCFF2021 11033182 8460989
FCFF2022 11416033
8192486
FCFF2023 11812170
7932503
Sum 51669421
After summarizing the free cash flow to firm for Sandivk, we calculate the terminal value.
Terminal value:9959956
(0,0686−0,02)= 204888 MSEK
PV terminal value: 204888237
(1+0,0686)= 191733 MSEK
If we add the sum of predicted future cash flow to firm to the terminal value, we obtain the
firm value of Sandvik.
𝐹𝑖𝑟𝑚 𝑣𝑎𝑙𝑢𝑒 𝑆𝑎𝑛𝑑𝑣𝑖𝑘=191733 + 51669 = 243402 MSEK
29
This is the total value, which includes both Sandviks debt and equity. The debt belongs to
Sandviks interest-bearing liabilities and is eliminated, by multiplying the total value with the
proportion of equity for Sandvik as shown:
243402 ∗ 0,6714= 163402 MSEK
We have now obtained the total equity value of Sandvik, and by dividing the total equity
value with the shares outstanding for the year in question, we arrive at a calculated price of
the share:
163′420′459′000
1′254′386′000= 130 SEK
A comparison of Sandviks equity according to the cash flow valuation and market value,
show that Sandvik is undervalued, since the closing price in 2018 was 125 SEK. When the
interest-bearing debt (32,86%) is deducted from the company value and this sum is then
divided with the number of shares outstanding, Sandvik receives a value per share according
to the FCFF-approach of 130 SEK. This should be considered as the target price in the long
run.
30
4.6 Valuation using Relative valuation
The relative valuation approach will be based on the four multiples presented in the theory
section. The choice of multiples is based on the fact that Damodaran means that the most
widely used equity multiples are price-earnings ratios, price to book value ratios and price to
sales ratios.
The table above shows how the procedure for valuing Sandvik has been executed. The
process is done in the same way for all companies. The valuation in this example is based on
fundamentals from 2014, and the comparable companies are the one in the same industry as
Sandvik. Means for the industry have been calculated, as this will form the basis for the
valuation of Sandvik. For the valuation based on the P/E-ratio, Sandviks earnings per share
are multiplied with the industry average P/E ratio. For the P/S, Sandviks sales per share are
multiplied with the industry average P/S ratio. According to what has been written in theory,
the P/B-multiple is given greater weight (40%) than the three other ratios (20% each). The
final calculated value is suggesting that Sandvik is now undervalued, and that the target price
should be 106,74 SEK. All multiples included in the valuation means, except for the Price /
Book Value, that Sandvik was undervalued in January 2014.
31
5 Results
The results from the valuations are presented in the chapter, with additional outputs from the
regression analysis. It is divided into two different sections, with the FCFF-approach first,
and then the results and observations obtained from the Relative valuation.
___________________________________________________________________________
5.1 Free cash flow to firm
To reconnect to the purpose of the study, we wanted to see how the calculated prices from all
three industries correlate with all the associated market prices. The regression equation we
primarily used to be able to see how much our calculated price could explain the market price
was:
After logging our variables and then performing a heteroscedasticity test, we found that the
regression model suffered from heteroscedasticity, which would damage the reliability of our
variables. The next step, in order to find the driver of heteroscedasticity, was to put a dummy
variable on each sector in three regression models. We could then conclude that the
Manufacturing sector contained extreme values, which would force the heteroscedasticity in
our model. To solve this problem, we regressed our calculated prices on the market prices
again, where we added a new independent variable, representing the dummy variable of the
manufacturing sector. This generates a new regression equation, which is described as:
32
After a new Breusch-Pagan heteroscedasticity test was made, we observed that the null
hypothesis of the model having constant variance could no longer be rejected on a 95%
confidence interval basis:
After running the new regression of market prices with the use of our calculated prices, there
are several factors we can observe. First of all, the t-statistics of our two coefficients reveal
that both of them are significant. The coefficient value of our calculated price (kprice)
explains that a one percent change in our calculated price leads to a 0,6 percent change in the
market price. The coefficient of determination (𝑅2) of 0,4726 means that approximately 47
percent of the variability of market prices is explained by the calculated prices. The null
hypothesis states that the calculated prices have no impact on the market price. We find that,
within a 95% confidence interval, the null hypothesis can be rejected according to our
regression model. In the scatter plot below, we can visualize the relationship between the two
variables. As can be noticed, there is a positive relationship between the calculated value and
the market price of the selected shares.
33
5.1.1 Industry comparison
The following three regressions refer to the industries being examined in order to distinguish
statistical differences between them.
Manufacturing
Bank/Real Estate
34
Consumer goods
By looking at the models, we can first state that each and every industry coefficient are
significant for the model, since all t-statistics reach a value outside the critical values of the t-
distribution (-1,96 to 1,96). This can be confirmed by looking at the p-values where we find
every p-value to be smaller than the alfa (𝛼) level of 0.05 in our regression model. Moreover,
we find that the coefficient for the consumer-goods industry of 0,7566 explains the market
price better compared to the other industries, followed up by the manufacturing industry
where one percent change in the predicted price indicates nearly a half percent change in the
market price, holding all others equal. The bank/Real Estate industry has the lowest number
of observations which to some extent, explains the relatively large standard error.
Additionally, what is notable is that the coefficient of determination is larger for the
consumer-goods industry, than for the other industries.
The next and final step is to examine if any of our independent variables has a coefficient
value that is equal to 1, I.e the calculated prices can explain the market price to a full extent.
To do that, we performed an additional one-sided t-test in STATA. The test is conducted by
stating two hypotheses, one states that the coefficient value of our independent variable is
equal to one (H0: β = 1). The other one represents the alternative hypothesis which states that
the coefficient value is different from 1 (Ha: β ≠ 1). The summary of the tests is shown
below:
F-statistic Prob > F Interpretation
All industries 35,3 0,0000 Reject H0
Manufacturing 27,35 0,0000 Reject H0
Bank/Real Estate 9,24 0,0043 Reject H0
Consumer-goods 12,95 0,0024 Reject H0
35
The four columns refer to the regression models previously performed, where the first one
summarizes all the calculated prices, and the three following columns refer to the specific
industry. Since all the coefficient values have P-values lower than the significance level
(a=0,05), we reject the null hypothesis, for all separate tests, that the coefficient value of our
independent variable is equal to 1.
5.2 Relative Valuation
As was conducted in the FCFF section, to look at the percentage change between our
calculated price and market price, we use a log-log model for the relative valuation
regression. The final output is shown below:
We tested for heteroscedasticity and observed that our model contained it. After putting in a
dummy variable for the manufacturing industry we were able to decrease the
heteroscedasticity, but not eliminate it. Before further analysis, it is important to consider that
when interpreting this regression model, the calculated price coefficient is less precise. This
increases the likelihood that our coefficient estimate is further from the real population value.
If we review the output, we find a coefficient value of the calculated price of 0,586, which is
significant in a 95% confidence interval, and the P-value of 0,000 confirms the significance.
It tells us that for a one percent unit increase in the calculated price, the market price is
expected to increase with 0,586 percent unit. The coefficient of determination reveals that
approximately 28% of the variability in the market price is caused by our independent
variable and the rest is explained by variables not included in our model. Since this regression
suffers from heteroscedasticity which could lead to unreliable and misleading results, we
36
would argue that we will neither reject nor reject the null hypothesis that the relative
valuation approach has no effect on the market price.
5.2.1 Industry comparison
Continuing, we will test if there are any differences between the industries the prices obtained
from the relative valuation approach.
Manufacturing
Bank/Real Estate
Consumer goods
37
Starting with observing the manufacturing industry we can directly see that the t-value is
insignificant which makes it clear that the relative valuation approach is not applicable in this
sector. Moving on to the other two regression models we can observe that in the Bank/Real
Estate industry we have a significant coefficient value of 1,01 which is interpreted as a one
percentage increase in the calculated price should increase the market price by 1,01 percent.
There is, in other words, a strong relationship between the calculated prices in the Bank/Real
Estate sector and the market prices in that sector. However, it is this industry that drives the
heteroscedasticity, which makes the estimate uncertain. The consumer goods industry has a
significant coefficient value of 0,76 which indicates a quite strong relationship with the
market prices. In order to compare between the industries, we can conclude that in the
Bank/Real Estate sector the relative valuation approach is good a method for predicting
market prices, even though you should be a bit careful in interpreting the coefficient value
due to the heteroscedasticity. In the consumer-goods industry we find that the calculated
values can explain the market prices of up to 76% whilst no interpretation can be done in the
manufacturing sector.
We end the result section by testing if any of the coefficient values obtained from our
regression model may fully explain the market prices, which would suggest that the
coefficient value has a value of 1. The one-sided t-test is used, and the hypotheses are stated
as follows:
H0: β = 1
Ha: β ≠ 1
F-statistic Prob > F Interpretation
All industries 27,14 0,0000 Reject H0
Manufacturing 82,85 0,0000 Reject H0
Bank/Real Estate 0,00 0,9441 Can’t reject H0
Consumer-goods 3,99 0,0506 Can’t reject H0
38
To start with, when examining the relationship between all the calculated prices obtained
from the relative valuation approach and the market prices, we found that this relationship
cannot take a value of 1, as the table shows. We found no statistical significance in the small
relationship between market prices and the manufacturing industry, so the interpretation of
this test is of low importance. The bank/Real Estate industry showed a significant relationship
of 1,01 with a standard error of 0,18 in the regression which makes it likely that the
coefficient value may equal 1, as this t-test suggests. What was more interesting is that this t-
test suggests that Consumer-goods industry with an F-statistic of 3,99, could completely
explain the market prices of stocks, even though it is not likely that this relationship would
stand in a 99% confidence interval.
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6 Discussion
In this section of our thesis we are going to present suggestions of how our purpose and
research questions may be responded. We divide this section into two parts where the first
part discusses and analyze the use of our chosen methods and the second part discuss and
analyze the results of our thesis.
_____________________________________________________________________
The purpose of this study was to look at how the two classic and widely used valuation
models of Free Cash Flow to Firm and Relative valuation can contribute to the explanation of
market prices of shares. The second mission of our study was to investigate if we could find
any significant differences between industries when using the two valuation approaches.
6.1 Method discussion
A part of great importance in our work was to interpret the theoretical formulas into reality,
in order to get a final calculated value that we could use for the regressions. In FCFF, we had
to make certain assumptions about the fundamentals of the model. According to what theory
suggests, we assumed one high growth phase, and one stable growth phase for all companies
involved. This is a complex matter, because in reality a stable growth period might appear at
different times for different companies. Therefore, the rate could be considered stable at
different levels. In order to be consistent, we fixed this rate to 2% for every company, to
proceed with the valuation process. The high growth rate implies intuitively that the company
in question has a growth of large level. In the valuation, we obtained the mean of the revenue
growth for the last five years and interpreted it as the high growth for each company. This
resulted in a few negative growth rates. To be consistent, we had to apply these rates into the
valuations. This may be an example of the difficulties of converting the theoretical model
into practice. When calculating the terminal value, it becomes clear that slight changes in cost
of capital have large impacts on the outcome. This indicates how important it is to strive for
the correct numbers in the different elements of the capital structure and financing costs. We
would say that this exemplifies the very essence of the subjective assessment that corporate
valuation implicates.
What is also important to analyze, is the assessment of how the upcoming years free cash
flow might look like. Since this is a key parameter to predict the future cash flow to the firm,
we based this number on how the previous three years FCFF have been and drew a mean
40
from them to predict the following years free cash flows. We think that this assumption is not
necessarily incorrect, since there is no assurance in predicting future cash flows. It is, though,
problematic since the cash flows fluctuate for many companies from year to year. The fact
that we use a mean omits the possibility to look at trends, for instance.
Even though we reached statistical significance using the FCFF-values, it is inevitably to
state that the model did not work for regression analysis without modifications. The
regression model is based on the method of least squares (OLS), in which an underlying
assumption is that the error term must be the same regardless of the value of X, in order to
have homoscedasticity. This assumption could be fulfilled only when introducing a dummy
variable to the regression model, which indicates that the FCFF method, exclusively, has
weaknesses.
The choice of companies was primarily chosen to be optimal from a DCF-perspective, where
we picked mature companies with good information about their assets. Assets that can
generate cash flows that can easily be forecasted. This is in line what Damodaran suggest,
that the models works best on companies with those characteristics. However, this does not
mean that the chosen sample is optimal from a relative valuation perspective, considering that
our two methods are so different from each other. By choosing companies from other
preferences, such as growth and risk, could have resulted in a fairer and more reasonable
outcome according to the relative valuation. This is an ambiguous matter since most
comparative analyzes is done with companies in the same industry, which motivated our
choice to conduct the method in such a way.
As mentioned before, there are no praxis on how to fold the multiples against each other, or
which multiples you choose to include in a relative valuation. We choose four different
multiples based on its differences, with regards to Penman's suggestions about how to weight
them together. However, it is important to consider that the final price will have different
values depending on the assessment of weighting these multiples; if some researchers and
investors would claim that other multiples are better for obtaining the true value of a
company, the stock price of our company would change substantially.
41
As in the case of FCFF, the Relative Valuation approach was not able to extract values that
would fit in a regression model where the assumption of homoscedasticity is fulfilled. This
implies that this method has difficulties to produce values that is statistically testable, which
should be considered while using it.
Only because certain observations will be excluded in the regression does not mean that these
values are ignored and forgotten in the study. Applying a particular model to a particular
company, which results in extreme values, could be seen from different perspectives. Most
likely, it is the company that has abnormal numbers, but it can also be seen as a criticism of
the model in question. The creation of extreme values from a model could be a sign that the
model is not always working and should be taken into consideration.
In the discussion of DCF-models in general, it is important to remember that these models
assume that markets make mistakes in evaluating stocks and therefore has a restrictive view
on market efficiency. It then occurs a methodological problem when finding out how close a
DCF-based calculated value is in relation to the market value and base the result on how good
of fit these two prices are. If the market is ineffective in pricing stocks and the discounted
cash flow models are better in assessing the ‘true’ value of a stock, the differences between
the market price and the price obtained from a DCF model are of low importance.
6.2 Result discussion
In terms of the FCFF valuation, we observe in the section where we regressed our calculated
prices on the market prices, that there is a correlation between them. This suggests accepting
our hypothesis that our predicted values would partly explain the market prices of the
selected shares.
In this regression, where we find a coefficient value for our FCFF variable of 0,6. We
interpret it as it is a medium-strong relationship and that was somewhat surprising for us,
because when we visually compared the final stock prices with the market prices, we
observed large differences on several stocks. Although this study cannot refer to earlier
results specifically, we can at some extent confirm that the FCFF method has fundamentals
that are important to derive the market price.
42
Previous research results about industry comparisons from a FCFF approach are rare. When
applying the FCFF method to companies in different sectors to find differences, it makes it
difficult to relate our research results to what already has been concluded, especially
regarding the three industries we have chosen for this thesis. Apart from that, observing our
results may indicate that the FCFF method applies better to the consumer-goods industry than
for the other industries. This statement should be cautiously considered since the number of
observations used in the three industries are somewhat small.
The relative valuation approach, where we used multiples to get a final stock price, we made
a regression analysis based on 169 observations and looked at how they were related to the
market prices. This regression did not meet the criterion of homoscedasticity, which could
have serious consequences for the OLS estimator which the regression is based upon. Even
though the estimator remains unbiased, the problem affects the standard error which by
extension affects the confidence interval and hypothesis testing. This would affect the
reliability of our results, because of the uncertainty of the relationship. This makes it hard to
draw conclusions about whether the relative valuation approach is a good method for
explaining the market prices. It is, anyhow, a common problem in many regression analyses,
and for the sake of answering the research questions we have to assume that the coefficient of
0,586 is adequate. The regression expresses a relationship between the calculated prices and
the market prices, but the variables do not correlate as much as in the case where the FCFF
values were regressed against the market prices.
To compare between the two regressions, it is relevant to convey that the sample size differs
between them considerably. In addition to this fact, it is remarkable that the multiple-based
calculations, which in turn rely on the market efficiency to a higher extent, have a lower
correlation with the market price, rather than otherwise. This would partly go against findings
made in the article Is cash flow king of valuations? where they found that the use of
multiples, and specifically earnings multiples, is the best measure of predicting market prices.
The industry comparison regression according to the use of multiples brought interesting
results. By looking at the predicted prices in the Bank/real estate industry we discovered a
strong relationship with the market prices. This relationship should not be interpreted as
certain, as it was this industry that the heteroscedasticity was detected in. The consumer
43
goods industry did also indicate a strong relationship with market prices, while the
manufacturing industry could not show any statistical evidence of explaining market prices.
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7 Conclusion
In the last chapter we present the conclusions of the thesis.
___________________________________________________________________________
In this thesis, two different valuation approaches were applied to several Swedish listed
companies. The purpose was to see how well the valuation methods worked on companies in
different industries.
In terms of the free cash flow to firm approach, we suggest that there is a positive relationship
between the values obtained from this method, and the market prices associated with these
values. Besides, we suggest that this positive relationship is found in the Consumer-goods
industry, the Bank/Real Estate industry and the Manufacturing industry, where the method is
best applied on the companies in the Consumer-goods industry. Furthermore, a limited
number of observations were used in the regression analysis, which may have affected the
study's results.
When using weighted industry multiples, we find that there is also a positive relationship
between the calculated prices and the market prices, even though we could not precise the
exact value of relationship due to the heteroscedasticity in our regression model. This method
was surprisingly applicable to the Bank/Real Estate sector and the Consumer-goods industry,
although the heteroscedasticity of this regression model needs to be taken into consideration.
When interpreting the results in this study, certain things must be taken into consideration.
Our selected sample might not be optimal for both our valuation approaches. The companies’
works well from a DCF perspective, since no surrounding effects affect the search for the
intrinsic value of a company. However, if the relative valuation was conducted in a way
where the comparable firms were based on similarities in cash flows, risk and growth, we
might have expected a different result. The comparative companies that we selected might be
too different from the company that is being evaluated, based on these fundamentals, and
may have had an impact on the final result.
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7.1 Suggestion for Further Research
In the future, it would be interesting to see a study that performs a relative valuation on a
larger sample with Swedish companies. In such a study, it could be possible to choose
comparable companies by looking at the growth rate, cash flows and risk, and not only
choose companies based on the fact that they are operating in the same industry. In a study
like that, it would be possible to look further into the multiples and see which of these a better
indicator of company valuation than others.
46
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Appendix
1. Relative valuation 2014
2. Relative valuation 2015
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3. Relative valuation 2016
4. Relative valuation 2017
51
5. Relative valuation 2018
52
6. FCFF 14
53
7. FCFF 15
54
8. FCFF 16
55
9. FCFF 17
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10. FCFF 18
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11. Companies (Thompson Reuters commands)
12. MRP calculation
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13. Risk free rate
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14. WACC calculations